AI in Food Banks: 5 of the Most Interesting Use Cases

We’ve been learning so much from the incredible participants in our AI cohort, “Ahead of the Curve: Generative AI for Food Bank Leaders.” This community of Feeding America food bank leaders across the country is learning how to ethically leverage AI to increase operational capacity and further their mission. 

In the first two weeks, we challenged participants to pick an AI tool and use it for everything for 3 days. We believe you can't talk about how your values align with AI use until you’ve seen what it can do. That led to the creation of 60 use cases, from predicting food volume to increasing the volunteer network to making marketing simpler. 

Here we’re sharing our five favorite ideas and suggested prompts for how AI can make operating a food bank easier and more effective — while remaining centered on people facing food insecurity.

(And if you’d like to see the full list of 60+ AI use cases for food banks, download a PDF copy here.)

5 of the Most Interesting Use Cases

Try these sample prompts with ChatGPT as you go. We have ideas for content creation, automation, strategy, research, and data analysis.

1. Fundraising Campaign Content Generation

Content creation is a fantastic use of AI for food bank staff who have tons to do each week and not a lot of time on their hands. AI can rapidly generate multiple versions of appeals for A/B testing. Think: emails, social posts, and direct mail copy, each tailored to different donor segments. 

What would take even the best marketing manager a few hours can be done in seconds, freeing up your staff’s time to do heavier-lift projects. With tailored copy for different uses, you’ll see more effective campaigns that pull in more donors.

Suggested prompt: “Attached are examples of past fundraising emails, donor segment profiles, and our upcoming campaign goals. Draft a fundraising campaign strategy, including three new donor appeal emails tailored to different segments (major donors, monthly donors, first-time givers), social posts for the duration of the campaign and suggestions for direct mail pieces. If you need more details about tone, past results, or campaign goals, ask me follow-up questions before finalizing.”

2. Volunteer Matching Bot

Your volunteer scheduling may currently look like: tons of Excel sheets, matching open slots to volunteer schedules, emails back and forth, and a lot of fighting with technology. That’s why we love this AI use case for a food pantry. AI can match volunteer preferences with pantry needs in seconds.

This prompt just requires you to input a general shift schedule and your volunteer database. Repeat each month to save yourself a huge headache. You can even ask AI to format this according to the volunteer platform you use!

This fast, automated scheduling makes sure no volunteer’s preferences are missed, which leads to stronger retention of volunteers, who have limited time to give. 

As with all AI use, make sure to double check the output! AI isn’t always correct and a thorough read-through will help to reduce unnecessary confusion among your volunteer network. 

Suggested prompt: "Use our upcoming volunteer shifts and our volunteer database with availability, skills, and location uploaded here. Generate a draft schedule that best matches volunteers to shifts. If there’s missing information (e.g., shift duration, skill requirements), ask me for clarification before completing the schedule.”

3. Strategic Innovation: Meeting, Missing, Matching

Our “meeting, missing, matching” framework helps organizations translate intentions into tangible action steps. AI can make this process easier by helping food banks see who they’re meeting with current programs, who they’re missing, and how well their services are matching community needs. 

By analyzing internal records and external data, AI flags disparities in age, geography, language, or access so you can spot where barriers exist.

Once gaps are clear, AI becomes a brainstorming partner for innovative programs. It can scan research and policy briefs, then suggest models tailored to neighbors you’re not yet reaching. 

We’re a fan of this use case for giving leaders a concrete plan to adapt their services to be an even better fit for their community. 

Suggested prompt: “Here are our service numbers and recent community data. Identify who we’re meeting, missing, and how we can match programs to the community's needs. Highlight service gaps, recommend three program ideas to reach missing groups, and model how those ideas could change demand for food or volunteers. Ask for any details you need before finishing.”

4. Synthesis of External Signals

Food bank leaders can use AI for easier, quicker, and more thorough research. Ask AI to scan policy briefs, reports, and news to flag likely community impacts for your demographic.

We love this use case because it keeps leadership better informed and frees up staff members to get more done with their limited time. While a dedicated analyst can be extremely helpful, ChatGPT can get the job done in seconds.

Suggested prompt: "There are several new policy briefs and news articles I could use help synthesizing. I've attached each of those here. Review and synthesize them into a one-page summary of key takeaways, flagging the three most likely ways these developments could impact food insecurity in our community. If you need more local context (e.g., our current client demographics or program capacity), ask me for it before finalizing."

5. Scenario Modeling

This question came up in our AI cohort: “When will the new SNAP and Medicaid changes from the One Big Beautiful Bill Act hit food pantries?" We thought we’d test out ChatGPT’s ability to help. AI can generate “what if” models, for example, “if there are X% SNAP cuts, how many additional households would need support?”

The forecast it created provided a lot of insights into what we could see happen as a result of the bill. Make sure to cross-check the results against information like local data and focus group comments to build a more well-rounded picture of the future. 

This data analysis won’t be perfect, but it’s a great starting point for forecasting and decision-making. This prompt can help you proactively plan and make a pitch to your board for upcoming needs.

Suggested prompt: "Using SNAP, Medicaid, and poverty data for [State/Food Bank Service Area], please forecast the impact of the OBBB Act on charitable food demand over the next 6, 12, and 24 months. Include county-by-county projections of absolute increases and per-capita burden, a table of top hotspots, and recommendations for operational planning."

Join Our Food Bank Leader Community

We’ve got more where this came from! If you’re a food bank leader and want to sharpen operations and streamline your strategy, we encourage you to be a part of our AI cohort. A lot of these next steps are best explored in community with other leaders in positions just like yours. Join the interest list to hear when the next cohort launches.